"""simple docstring"""







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	List[str]=None	,     __SCREAMING_SNAKE_CASE				:	str=None ):
     return field(default_factory=lambda: default	,     metadata=a__ )







@dataclass
class 			UpperCamelCase						:
   lowercase				=    field(
       metadata={'help': 'The csv file to plot.'}		, )
   lowercase				=    field(
       default=lowercase_		, metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'}		, )
   lowercase				=    field(
       default=lowercase_		, metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'}		, )
   lowercase				=    field(
       default=lowercase_		, metadata={'help': 'Disable logarithmic scale when plotting'}		, )
   lowercase				=    field(
       default=lowercase_		, metadata={
           'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
       }		, )
   lowercase				=    field(
       default=lowercase_		, metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'}		, )
   lowercase				=    list_field(
       default=lowercase_		, metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}    )






def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any ):






     try:
          int(a__ )
          return True
     except ValueError:
          return False



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	int ):






     try:
          float(a__ )
          return True
     except ValueError:
          return False







class 			UpperCamelCase						:
   def __init__(     self	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        lowercase_      :							Dict						=      args
        lowercase_      :							List[Any]						=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

        with open(self.args.csv_file	,newline=''     ) as csv_file:
             lowercase_      :							Optional[int]						=      csv.DictReader(__UpperCamelCase     )
             for row in reader:
                  lowercase_      :							Optional[int]						=      row['model']
                  self.result_dict[model_name]["bsz"].append(int(row['batch_size']     )     )
                  self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']     )     )
                  if can_convert_to_int(row['result']     ):
                       # value is not None
                       lowercase_      :							List[str]						=      int(row['result']     )
                  elif can_convert_to_float(row['result']     ):
                       # value is not None
                       lowercase_      :							Any						=      float(row['result']     )







   def      _UpperCAmelCase  (     self     )      -> List[str]:


        '''simple docstring'''


        lowercase_      :							int						=      plt.subplots()
        lowercase_      :							Dict						=      'Time usage' if self.args.is_time else 'Memory usage'
        lowercase_      :							List[str]						=      title_str + ' for training' if self.args.is_train else title_str + ' for inference'

        if not self.args.no_log_scale:
             # set logarithm scales
             ax.set_xscale('log'     )
             ax.set_yscale('log'     )

        for axis in [ax.xaxis, ax.yaxis]:
             axis.set_major_formatter(ScalarFormatter()     )

        for model_name_idx, model_name in enumerate(self.result_dict.keys()     ):
             lowercase_      :							Optional[Any]						=      sorted(set(self.result_dict[model_name]['bsz']     )     )
             lowercase_      :							List[str]						=      sorted(set(self.result_dict[model_name]['seq_len']     )     )
             lowercase_      :							Optional[Any]						=      self.result_dict[model_name]['result']

             (lowercase_)      :							Optional[Any]						=      (
                 (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
             )

             lowercase_      :							Optional[Any]						=      (
                 model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
             )

             for inner_loop_value in inner_loop_array:
                  if self.args.plot_along_batch:
                       lowercase_      :							Dict						=      np.asarray(
                           [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]	,dtype=__UpperCamelCase	,)
                  else:
                       lowercase_      :							int						=      np.asarray(
                           [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]	,dtype=np.floataa	,)

                  (lowercase_)      :							Optional[int]						=      (
                      ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
                  )

                  lowercase_      :							List[str]						=      np.asarray(__UpperCamelCase	,__UpperCamelCase     )[: len(__UpperCamelCase     )]
                  plt.scatter(
                      __UpperCamelCase	,__UpperCamelCase	,label=f'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''     )
                  plt.plot(__UpperCamelCase	,__UpperCamelCase	,'--'     )

             title_str += f''' {label_model_name} vs.'''

        lowercase_      :							Optional[Any]						=      title_str[:-4]
        lowercase_      :							Optional[Any]						=      'Time in s' if self.args.is_time else 'Memory in MB'

        # plot
        plt.title(__UpperCamelCase     )
        plt.xlabel(__UpperCamelCase     )
        plt.ylabel(__UpperCamelCase     )
        plt.legend()

        if self.args.figure_png_file is not None:
             plt.savefig(self.args.figure_png_file     )
        else:
             plt.show()






def      lowercase__(			):
     lowercase_      :							List[str]						=      HfArgumentParser(a__ )
     lowercase_      :							List[Any]						=      parser.parse_args_into_dataclasses()[0]
     lowercase_      :							int						=      Plot(args=a__ )
     plot.plot()


if __name__ == "__main__":
       main()









"""simple docstring"""


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
		from PIL import Image




class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Optional[Any]      =  """Salesforce/blip-image-captioning-base"""
					lowercase_    :				Optional[int]      =  (
					    """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
					    """image to caption, and returns a text that contains the description in English."""
					)
					lowercase_    :				int      =  """image_captioner"""
					lowercase_    :				int      =  AutoModelForVisionaSeq

					lowercase_    :				List[str]      =  ["""image"""]
					lowercase_    :				int      =  ["""text"""]
					def __init__(							self							:	Any     , *UpperCAmelCase							:	str     , **UpperCAmelCase							:	List[str]   )	->       Union[str, Any]:
											requires_backends(self     , ['vision']   )
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	"Image"   )	->       Union[str, Any]:
											return self.pre_processor(images=UpperCAmelCase     , return_tensors='pt'   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Optional[int]   )	->       Optional[int]:
											return self.model.generate(**UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	int   )	->       Dict:
											return self.pre_processor.batch_decode(UpperCAmelCase     , skip_special_tokens=UpperCAmelCase   )[0].strip()